Tomoe Ishikawa

Astrocyte calcium signalling orchestrates neuronal synchronization in organotypic hippocampal slices

In the brain, astrocytes detect neuronal activity and regulate neuronal excitability and synaptic transmission. Recent studies show that calcium elevations that are localized within astrocyte processes upregulate endogenous neurotransmission at nearby synapses. We demonstrated that at the network level calcium buffering in astrocytes caused a significant reduction in the correlated activity of neurons in cultured hippocampal slices. In contrast, the uncaging of calcium in astrocytes triggered synchronized activity in neuronal populations. This study provides experimental support for the functional relevance of astrocyte signalling to the maintenance of collective neuronal dynamics.

Dopamine Receptor Activation Reorganizes Neuronal Ensembles during Hippocampal Sharp Waves In Vitro

Hippocampal sharp wave (SW)/ripple complexes are thought to contribute to memory consolidation. Previous studies suggest that behavioral rewards facilitate SW occurrence in vivo. However, little is known about the precise mechanism underlying this enhancement. Here, we examined the effect of dopaminergic neuromodulation on spontaneously occurring SWs in acute hippocampal slices. Local field potentials were recorded from the CA1 region. A brief (1 min) treatment with dopamine led to a persistent increase in the event frequency and the magnitude of SWs. This effect lasted at least for our recording period of 45 min and did not occur in the presence of a dopamine D1/D5 receptor antagonist. Functional multineuron calcium imaging revealed that dopamine-induced SW augmentation was associated with an enriched repertoire of the firing patterns in SW events, whereas the overall tendency of individual neurons to participate in SWs and the mean number of cells participating in a single SW were maintained. Therefore, dopaminergic activation is likely to reorganize cell assemblies during SWs.

Simultaneous silence organizes structured higher-order interactions in neural populations

Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.

Hippocampal ripples down-regulate synapses

Rebalancing mechanisms during sleep Synapses are often strengthened during wake periods and thus need to be homeostatically readjusted during sleep. During slow-wave sleep, synaptic depression is dominant. Sharp wave and ripple events are transient high-frequency field oscillations that occur spontaneously during slow-wave sleep in the brain. Norimoto et al. found that these events induced long-term depression of hippocampal synapses and may thus help to refine recently acquired memories (see the Perspective by Draguhn). Science, this issue p. 1524; see also p. 1461 Sharp-wave ripple events in slow-wave sleep induce long-term depression at hippocampal synapses in sleeping mice. The specific effects of sleep on synaptic plasticity remain unclear. We report that mouse hippocampal sharp-wave ripple oscillations serve as intrinsic events that trigger long-lasting synaptic depression. Silencing of sharp-wave ripples during slow-wave states prevented the spontaneous down-regulation of net synaptic weights and impaired the learning of new memories. The synaptic down-regulation was dependent on the N-methyl-d-aspartate receptor and selective for a specific input pathway. Thus, our findings are consistent with the role of slow-wave states in refining memory engrams by reducing recent memory-irrelevant neuronal activity and suggest a previously unrecognized function for sharp-wave ripples.

Ex vivo cultured neuronal networks emit in vivo-like spontaneous activity.

Spontaneous neuronal activity is present in virtually all brain regions, but neither its function nor spatiotemporal patterns are fully understood. Ex vivo organotypic slice cultures may offer an opportunity to investigate some aspects of spontaneous activity, because they self-restore their networks that collapsed during slicing procedures. In hippocampal networks, we compared the levels and patterns of in vivo spontaneous activity to those in acute and cultured slices. We found that the firing rates and excitatory synaptic activity in the in vivo hippocampus are more similar to those in slice cultures compared to acute slices. The soft confidence-weighted algorithm, a machine learning technique without human bias, also revealed that hippocampal slice cultures resemble the in vivo hippocampus in terms of the overall tendency of the parameters of spontaneous activity.

Multineuronal spike sequences repeat with millisecond precision

Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging (fMCI) technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and non-spiking neurons. Multineuronal spike sequences (MSSs) were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

A Computationally Efficient Filter for Reducing Shot Noise in Low S/N Data

Functional multineuron calcium imaging (fMCI) provides a useful experimental platform to simultaneously capture the spatiotemporal patterns of neuronal activity from a large cell population in situ. However, fMCI often suffers from low signal-to-noise ratios (S/N). The main factor that causes the low S/N is shot noise that arises from photon detectors. Here, we propose a new denoising procedure, termed the Okada filter, which is designed to reduce shot noise under low S/N conditions, such as fMCI. The core idea of the Okada filter is to replace the fluorescence intensity value of a given frame time with the average of two values at the preceding and following frames unless the focused value is the median among these three values. This process is iterated serially throughout a time-series vector. In fMCI data of hippocampal neurons, the Okada filter rapidly reduces background noise and significantly improves the S/N. The Okada filter is also applicable for reducing shot noise in electrophysiological data and photographs. Finally, the Okada filter can be described using a single continuous differentiable equation based on the logistic function and is thus mathematically tractable.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

BACKGROUND Calcium imaging has become a fundamental modality for studying neuronal circuit dynamics both in vitro and in vivo. However, identifying calcium events (CEs) from spectral data remains laborious and difficult, especially since the signal-to-noise ratio (SNR) often falls below 2. Existing automated signal detection methods are generally applied at high SNRs, leaving a large need for an automated algorithm that can accurately extract CEs from fluorescence intensity data of SNR 2 and below. NEW METHOD In this work we develop a Matched filter for Multi-unit Calcium Event (MMiCE) detection to extract CEs from fluorescence intensity traces of simulated and experimentally recorded neuronal calcium imaging data. RESULTS MMiCE reached perfect performance on simulated data with SNR ≥ 2 and a true positive (TP) rate of 98.27% (± 1.38% with a 95% confidence interval), and a false positive(FP) rate of 6.59% (± 2.56%) on simulated data with SNR 0.2. On real data, verified by patch-clamp recording, MMiCE performed with a TP rate of 100.00% (± 0.00) and a FP rate of 2.04% (± 4.10). COMPARISON WITH EXISTING METHOD(S) This high level of performance exceeds existing methods at SNRs as low as 0.2, which are well below those used in previous studies (SNR ≃ 5-10). CONCLUSION Overall, the MMiCE detector performed exceptionally well on both simulated data, and experimentally recorded neuronal calcium imaging data. The MMiCE detector is accurate, reliable, well suited for wide-spread use, and freely available at sites.uci.edu/aggies or from the corresponding author.

A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems.

GABAergic inhibition reduces the impact of synaptic excitation on somatic excitation

The effect of excitatory synaptic input on the excitation of the cell body is believed to vary depending on where and when the synaptic activation occurs in dendritic trees and the spatiotemporal modulation by inhibitory synaptic input. However, few studies have examined how individual synaptic inputs influence the excitability of the cell body in spontaneously active neuronal networks mainly because of the lack of an appropriate method. We developed a calcium imaging technique that monitors synaptic inputs to hundreds of spines from a single neuron with millisecond resolution in combination with whole-cell patch-clamp recordings of somatic excitation. In rat hippocampal CA3 pyramidal neurons ex vivo, a fraction of the excitatory synaptic inputs were not detectable in the cell body against background noise. These synaptic inputs partially restored their somatic impact when a GABAA receptor blocker was intracellularly perfused. Thus, GABAergic inhibition reduces the influence of some excitatory synaptic inputs on the somatic excitability. Numerical simulation using a single neuron model demonstrates that the timing and locus of a dendritic GABAergic input are critical to exert this effect. Moreover, logistic regression analyses suggest that the GABAergic inputs sectionalize spine activity; that is, only some subsets of synchronous synaptic activity seemed to be preferably passed to the cell body. Thus, dendrites actively sift inputs from specific presynaptic cell assemblies.